Google Antigravity: el disruptor que cambió el mundo de la computación para siempre

¡Tu sistema informático acaba de convertirse en tu kit de herramientas de agente de IA universal para todo! ¿Qué pasaría si pudieras hablar con tu ordenador de la misma manera que hablas con un compañero de trabajo, y eso simplemente lo hizo? What if you could talk to your computer the way you talk to a colleague — and it just ? did the work Hizo el trabajo Not autocomplete. Not a chatbot that pastes code snippets. Not a suggestion engine that highlights the next three words. A system that takes your intent and orchestrates your entire machine — files, browser, terminal, internet — to deliver the result. That is exactly what Google Antigravity does. Y aquí está la parte que debería detenerte en medio de la navegación: no es solo para los codificadores. And here's the part that should stop you mid-scroll: it's not just for coders. Sí, Antigravity puede construir una API de pilas completas desde cero, configurar una arquitectura de microservicios, generar un tubo de CI / CD y escribir 200 pruebas de unidades en el tiempo que tarda en preparar el café. Sus capacidades de codificación son incomparables - no es Escribe el código, es , produciendo artefactos de verificación (planes de implementación, capturas de pantalla, grabaciones de navegador) para que pueda revisar lo que se hizo antes de aprobarlo. Asistencia orchestrates agents that write, test, debug, and deploy code autonomously Pero aquí está la parte sorprendente: su ordenador es ahora su API. But here is the mind-blowing part: your computer is now your API. Every file on your hard drive, every tab in your browser, every URL on the internet, every command in your terminal — all of it is now a tool that autonomous AI agents can access, read, analyze, and act upon. Cuando la interfaz de su máquina es el lenguaje natural y el ejecutor es un agente de IA con las manos del navegador, el acceso al sistema de archivos y los ojos de Internet, la distinción entre "herramienta de desarrollador" y "herramienta profesional" se evapora completamente. When the interface to your machine is natural language and the executor is an AI agent with browser hands, file system access, and internet eyes, the distinction between "developer tool" and "professional tool" evaporates completely. Un investigador dice: "Lea estos 50 PDFs, busque en Google Scholar y produzca una revisión sistemática de la literatura". Done. Un gerente de marketing dice: "Visite estos tres sitios web de los competidores, analice sus precios y crea un análisis SWOT". Done. Un estudiante dice: "Leer mi plan de estudios, encontrar recursos gratuitos en línea, generar un plan de estudio de 6 semanas con tarjetas flash." Done. Un creador de contenido dice: "Escribe un post de blog técnicamente exacto sobre WebSockets, prueba cada fragmento de código ejecutándolo y optimice SEO para todo." Done — and the code was verified on your local machine before it was published. This isn't a coding tool that happens to do other things. This is a universal orchestration platform that happens to be good at coding as well. En este artículo, te mostraremos exactamente qué es Google Antigravity, los LLM que lo alimentan, cómo turbochargarlo con servidores MCP y backends de código abierto, y - Most importantly — 30 prompts detallados y optimizados por IA que abarcan seis profesiones que demuestran que Antigravidad no es el futuro de la codificación. 30 detailed, AI-optimized prompts spanning six professions that prove Antigravity isn't the future of coding. Es el futuro de la computación misma. It's the future of computing itself. ¡Vamos a sumergirnos! Google Antigravity: el orquestador de agentes de IA sin código de propósito general que cambia todo Google Antigravity no es sólo otro editor de código, es el primer entorno de desarrollo integrado (IDE) del mundo, lanzado por Google en previsión pública el 18 de noviembre de 2025. Google Antigravity is not just another code editor — it is the world's first agent-first integrated development environment (IDE), launched by Google in public preview on November 18, 2025. Construido sobre una filosofía radicalmente diferente de los asistentes de codificación tradicionales, Antigravity representa un cambio de paradigma desde la codificación asistida por IA al desarrollo orquestado por IA. Built on a radically different philosophy from traditional coding assistants, Antigravity represents a paradigm shift from to AI-orchestrated development. AI-assisted coding Codificación asistida En lugar de ofrecer sugerencias automáticas o fragmentos de código basados en chat, Antigravity implementa agentes de IA totalmente autónomos que planifican, escriben, prueban, borran y desplegan proyectos de software enteros en su nombre. El Alf. Instead of offering autocomplete suggestions or chat-based code snippets, Antigravity deploys fully autonomous AI agents that plan, write, test, debug, and deploy entire software projects on your beh But to focus on coding alone ignores the real power of Antigravity. The real secret that very few know is that Google Antigravity is not just for coding. Todo lo que se puede hacer en un ordenador. It is for everything that can be done on a computer. Everything! AI Agents can be used for anything you want! Your Computer is now an Automated AI Agent system with Google Antigravity. To highlight this, I have curated 30 prompts that will save you hours of work. Only 10 prompts out of 30 prompts are for developers! The Three Pillars of Power 1. Browser Integration Antigravity cuenta con un subagente dedicado del navegador con una extensión de Chrome que permite a los agentes de la IA interactuar con las páginas web como un ser humano: hacer clic en botones, desplazarse, escribir en formularios, leer contenido DOM y capturar capturas de pantalla. Esto permite la prueba automática de la interfaz de usuario, el rascado web, el relleno de formularios y el borrado en vivo de aplicaciones web a través de una capa de percepción visual y un mapa de árbol de accesibilidad. 2. Internet Access A diferencia de los asistentes de codificación aislados, los agentes de Antigravity pueden buscar en la web, obtener documentación, leer URLs y extraer datos en tiempo real de cualquier fuente accesible al público. Esto significa que su agente de IA puede buscar la última documentación de la API, encontrar soluciones de Stack Overflow o investigar las mejores prácticas, todo esto mientras construye su proyecto. 3. Local System Access Los agentes anti-gravidad operan directamente dentro de su sistema de archivos local, editor de código y terminal. Pueden ejecutar comandos de shell, ejecutar scripts de construcción, navegar por directorios, crear y modificar archivos y gestionar estructuras de proyectos. La seguridad se mantiene a través de entornos de caja de arena, políticas de ejecución automática de comandos de terminal, listas de permiso y listas de denegación: las acciones sensibles siempre requieren la aprobación explícita del usuario. ¿Por qué es diferente a cualquier otro asistente? No es la primera vez que esto es posible. Claude Desktop was released recently, and it is a very good product. But Google Antigravity blows it out of the water with token limits, generous refresh rates, and sheer breadth of capabilities. En cuanto a codificación: Asistentes tradicionales como GitHub Copilot o ChatGPT ofrecen —Todavía escribe el código. Sugerencias La antigravedad es una . code orchestrator Describe lo que desea a un alto nivel, y varios agentes de IA colaboran de forma asíncrona para entregarlo. La plataforma ofrece a donde puede generar, observar y coordinar a los agentes que trabajan en diferentes espacios de trabajo simultáneamente. Manager View (Mission Control) Los agentes producen Planes de implementación, listas de verificación de tareas, capturas de pantalla, grabaciones de navegador y difusiones de código, para que siempre sepa exactamente lo que se hizo y pueda revisarlo antes de la aprobación. verification artifacts Esto transforma al desarrollador de un escritor de código en un dirigir a los agentes autónomos. software architect Google Antigravity ya está disponible , y es Durante la previsión pública con generosos límites de tasa para el uso de LLM. Windows, macOS, and Linux free for individuals It completely redefines what it means to work on a computer system. But - Everything can be automated! Not just code! Los LLMs que impulsan Google Antigravidad Google Antigravity ofrece acceso a — la línea de LLM más diversa en cualquier herramienta hoy: multi-model ecosystem Model Provider Strength (High/Low) Gemini 3 Pro Google Primary agent model, planning & execution Gemini 3 Flash Google Fast inference, checkpointing Gemini 3 Deep Think Google Deep reasoning, complex architecture Gemini 2.5 Pro UI Checkpoint Google Browser subagent, UI interactions Gemini 2.5 Flash Google Context summarization Gemini 2.5 Flash Lite Google Codebase semantic search Claude Opus 4.6 (Thinking) Anthropic Advanced planning, large context windows Claude Opus 4.5 (Thinking) Anthropic Debugging, complex code review Claude Sonnet 4.5 (Thinking) Anthropic Balanced speed & quality GPT-OSS-120B OpenAI (Open Source) Open-weight, community-driven tasks (Alto y bajo) Gemini 3 Pro Google Modelo de agente principal, planificación y ejecución Gemini 3 Flash Google Inferencia rápida, checkpointing Gemini 3 Deep Think Google Razonamiento profundo, arquitectura compleja Gemini 2.5 Pro UI Checkpoint Google Subagente del navegador, interacciones de UI Gemini 2.5 Flash Google Contexto de resumen Gemini 2.5 Flash Lite Google Codebase semantic search Claude Opus 4.6 (Thinking) Antropología Planificación avanzada, grandes ventanas de contexto Claude Opus 4.5 (Thinking) Antropología Debugging, complex code review Claude Sonnet 4.5 (Thinking) Antropología Balanced speed & quality GPT-OSS-120B OpenAI (Open Source) Open-weight, community-driven tasks Los usuarios pueden seleccionar manualmente modelos por tarea, proporcionando una flexibilidad sin precedentes. No other product offers this breadth of frontier AI models in a single unified interface. As an Indian, I have free access to a suscribirse gracias a su vinculación con Google AI Pro Jio. El pago de Google Gemini fue bueno. El pago Google Banana Pro fue genial. But a paid subscription to Google Antigravity is irreplaceable. Turbocharge Antigravidad: Servidores MCP y backends de código abierto Connecting MCP Servers El is an open protocol by Anthropic that lets Antigravity connect to external tools, databases, and services. To add MCP servers: Model Context Protocol (MCP) Abre el panel de agentes → haga clic en ⋯ (más) → MCP Servers → Manage MCP Servers Click to open View raw config mcp_config.json Add your server configuration in JSON format and save Los servidores MCP incluyen , y los servidores. GitHub, Slack, Google Drive, Jupyter, File System SSH Connecting Claude Code with Cloud-Based Open-Source Models GLM-4/5 (Zhipu AI / BigModel API) Zhipu AI provides a high-concurrency API for the GLM series. Use the or Identificadores para el mejor rendimiento con tareas de codificación. glm-4 glm-4-plus And now has been released as well! glm-5 # 1. Set your Zhipu AI API Key export ANTHROPIC_API_KEY=your_zhipu_api_key # 2. Point Claude Code to the Zhipu OpenAI-compatible endpoint export ANTHROPIC_BASE_URL=https://open.bigmodel.cn/api/paas/v4 # 3. Launch Claude Code (Specify the model if the tool allows) claude model="glm-5" Kimi k2.5 (Moonshot AI API) Los modelos Kimi de Moonshot AI excelen en el procesamiento de largo contexto y el razonamiento complejo. Su API es totalmente compatible con OpenAI. # 1. Set your Moonshot API Key export ANTHROPIC_API_KEY=your_moonshot_api_key # 2. Configure the Base URL for Moonshot export ANTHROPIC_BASE_URL=https://api.moonshot.cn/v1 # 3. Launch Claude Code claude model="kimi-latest" DeepSeek R1 (API oficial de DeepSeek) The official DeepSeek API is the most cost-effective way to access the full R1 reasoning model without needing local multi-GPU clusters. # 1. Set your DeepSeek API Key export ANTHROPIC_API_KEY=your_deepseek_api_key # 2. Point to DeepSeek's official API endpoint export ANTHROPIC_BASE_URL=https://api.deepseek.com/v1 # 3. Launch Claude Code with the Reasoning model claude model="deepseek-reasoner" Important Notes on Cloud Integration Compatibilidad de API: La mayoría de estos proveedores utilizan el sufixo /v1 para la compatibilidad OpenAI, que Claude Code normalmente espera cuando supera la URL base. Ensure your environment variable contains the provider's key; even though the variable name says "Anthropic," the custom will route it to the correct provider. Key Masking: ANTHROPIC_API_KEY BASE_URL Cloud providers have tier-based rate limits. For heavy coding sessions, check your credit balance at the respective consoles. Rate Limits: This will help you if you are unable to use a paid Google AI Pro subscription! This will help you if you are unable to use a paid Google AI Pro subscription! One more key point before we dive into the prompts. The best LLM in Google Antigravity is not Google Gemini Pro 3.0. The best LLM in Google Antigravity is not Google Gemini Pro 3.0. Se trata de Claude Opus 4.6. It is Claude Opus 4.6. By far! For every prompt in this article, use Claude Opus 4.6 as of today (16th Feb 2026) for best results! For every prompt in this article, use Claude Opus 4.6 as of today (16th Feb 2026) for best results! Antes de empezar: 10 mejores prácticas para usar Google Antigravity de forma segura The Cautionary Tale That Shook the Developer Community In December 2025, barely a month after Antigravity's public launch, a developer made headlines across , de , , and Por la peor razón posible: Tom's Hardware Tecnólogo The Register How-To Geek El agente de inteligencia artificial de Google Antigravity borró toda su D: Drive - de forma permanente. Google Antigravity's AI agent wiped his entire D: drive — permanently. The developer had issued a routine instruction to clear a project cache. But operating in Turbo mode with aggressive auto-execution settings, the AI agent misinterpreted the scope of the command. Instead of targeting the project folder, it executed a recursive deletion command against the root of the entire drive — and used the /q (quiet) flag, which bypassed all confirmation prompts and the Recycle Bin. Months of work, personal files, project repositories — gone in seconds. Months of work, personal files, project repositories — gone in seconds. The AI agent itself then issued an apology, calling it a "critical failure." Este incidente no fue un bug en el sentido tradicional. — a developer gave an autonomous agent too much power without safeguards. trust calibration failure Antigravity is extraordinarily capable, but that power demands respect. The following 10 best practices, drawn from this incident and the broader community experience, will ensure you harness Antigravity's full potential without risking your data. The following 10 best practices, drawn from this incident and the broader community experience, will ensure you harness Antigravity's full potential without risking your data. The 10 Best Practices For Using Google Antigravity Turbo mode auto-executes commands without confirmation. Reserve it for sandboxed test projects only — never on drives containing irreplaceable data. Never enable Turbo mode on sensitive directories. Before handing control to an AI agent, create a snapshot. Use Git commits, cloud backups, or disk images. If the agent makes a catastrophic mistake, you can roll back in minutes. Back up before every major session. Always set a specific project directory as your workspace. Never point Antigravity at a drive root ( , ) or a home directory — scope its access as narrowly as possible. Use a dedicated workspace folder. C:\ D:\ Antigravity supports Terminal Command Auto Execution policies. Explicitly deny destructive commands ( , , ) and whitelist only the tools your project actually needs. Configure Allow Lists and Deny Lists. rm -rf del /s /q format Antigravity generates implementation plans, task lists, and diffs before executing. The few minutes spent reviewing can prevent hours of disaster recovery. Always review artifacts before approving. Read them. Before running a complex prompt on your real codebase, test it on a throwaway copy. Validate the agent's behavior in a safe environment before trusting it with production code. Test prompts on dummy projects first. "Clean up this project" is dangerous. "Delete all files in the directory" is safe. The more precise your prompt, the less room the agent has to misinterpret your intent. Be specific, not vague. .pyc src/ For large operations, break your prompt into stages and review after each stage completes. Don't ask the agent to "refactor everything and deploy" in a single prompt. Use incremental approval for multi-step tasks. Always work inside a Git repository with frequent commits. If an agent makes unwanted changes, is your instant undo button. Keep version control active. git reset --hard Watch the terminal output in real time. If you see a command targeting unexpected paths or using unfamiliar flags, using the interrupt function. 📊 Monitor the agent's terminal commands. stop the agent immediately Caution Google Antigravity is the most powerful AI tool ever created — and with great power comes great responsibility. Google Antigravity is the most powerful AI tool ever created — and with great power comes great responsibility. These best practices aren't optional. They are the difference between a 10x productivity boost and a career-defining disaster. Follow them religiously. Follow them religiously. With that, here we go! (Alle, alle, alle) 30 Game-Changing Prompts for Every Professional in All Domains The Developers Section 1: Full-Stack API Development with Auto-Testing Construir una API completa significa crear el motor back-end que potencia sitios web y aplicaciones, la máquina invisible que maneja las solicitudes de los usuarios, almacena datos y aplica seguridad. - Traditionally, this requires days of manual setup: designing database structures, writing endpoints, configuring authentication, and creating tests. - This prompt asks Antigravity to build the entire system from scratch — models, endpoints, authentication, testing, documentation, and deployment — in one go. - The result is a production-ready API that would normally take an experienced developer over a week to deliver. Google Antigravity Prompt: Build a production-ready REST API for [your-domain, e.g., e-commerce product catalog] using [framework: e.g., FastAPI/Express.js/Spring Boot]: Architecture: Design a clean MVC/service-repository pattern with dependency injection Models: Create database models for [entities, e.g., products, categories, reviews] with proper relationships, indexes, and validation constraints Endpoints: Implement full CRUD operations with: Pagination (cursor-based) Filtering by [fields, e.g., price range, category] Sorting (multi-field) Rate limiting (100 req/min) Auth: JWT authentication with refresh tokens and role-based access control Testing: Generate comprehensive pytest/jest test suite covering: Unit tests for all service methods Integration tests for each endpoint Edge cases (empty DB, invalid inputs, auth failures) Docs: Auto-generate OpenAPI/Swagger documentation Deploy: Create Dockerfile and docker-compose.yml with PostgreSQL Use [your project root: e.g., C:/Projects/my-api] as the workspace. Why This Is a Game-Changer: Eliminates 8–12 hours of boilerplate setup, architecture decisions, and test writing Asegura las mejores prácticas de la industria (paginación de cursores, limitación de la tasa, RBAC) que los desarrolladores a menudo saltan bajo presión temporal Las pruebas generadas automáticamente capturan los errores antes de llegar a la producción Produces deployment-ready code, not prototypes ⏱ Time Saved: ~10 hours manually → 15 minutes with Antigravity 2: Refactorización y modernización de la base de códigos heredada - Every software team inherits old code — code written by people who have left, using outdated patterns, with hidden security vulnerabilities, and no documentation. - Refactoring means restructuring this code to be cleaner, safer, and more maintainable without changing what it does. - This prompt instructs Antigravity to audit the entire codebase for problems, modernize dependencies, apply best practices, add type safety, write tests, and document everything. - The output is a clean, modern codebase with a detailed before-and-after report — weeks of risky manual work compressed into a single session. Google Antigravity Prompt: Analyze and refactor the legacy codebase at [path, e.g., C:/Projects/legacy-app]: Audit: Scan all files for: Code smells (duplicated logic, god classes, long methods) Security vulnerabilities (SQL injection, XSS, hardcoded secrets) Deprecated API usage and outdated dependencies Dependency Update: Upgrade all packages to latest stable versions, resolve breaking changes Architecture Refactor: Extract repeated logic into shared utility modules Apply SOLID principles to classes exceeding 200 lines Introduce proper error handling with custom exception classes Type Safety: Add TypeScript types / Python type hints to all functions Testing: Write unit tests for all refactored modules achieving >80% coverage Documentation: Generate JSDoc/docstring documentation for all public APIs Report: Create REFACTORING_REPORT.md with before/after metrics Preserve all existing functionality — run existing tests after each change. Why This Is a Game-Changer: Transforms weeks of dangerous manual refactoring into a safe, verified, automated process Catches security vulnerabilities that manual code review frequently misses Conserva la compatibilidad retroactiva mediante la ejecución continua de las pruebas existentes Produces a comprehensive report that satisfies management and compliance audits ⏱ Time Saved: 40+ hours manually → 40 minutes with Antigravity Creación automática de tuberías CI/CD - CI/CD (Continuous Integration / Continuous Deployment) is the automated assembly line that builds, tests, and delivers software every time a developer makes a change. - Without it, teams manually build, test, and deploy — a slow, error-prone process that leads to broken releases. Este prompt tiene Antigravity crear toda la tubería: construcción de automatización, pruebas, escaneo de seguridad, despliegue de etapa, despliegue de producción con rollback, y código de infraestructura. El resultado es un sistema de entrega de software de nivel profesional que captura los errores antes de que los usuarios los vean y se implementa con tiempo de inactividad cero. Google Antigravity Prompt: Create a complete CI/CD pipeline for [repo URL or local path] targeting [platform: GitHub Actions/GitLab CI/Jenkins]: Build Stage: Configure multi-stage build with caching for [language: Node.js/Python/Java] Test Stage: Run unit tests with coverage reporting (minimum 80%) Run linting ([ESLint/Pylint/Checkstyle]) Run security scanning (Snyk/Trivy container scan) Quality Gates: Fail pipeline if coverage drops below threshold or critical vulnerabilities found Staging Deploy: Auto-deploy to [staging environment URL] on PR merge to develop Production Deploy: Deploy to [production environment] on tagged releases with: Blue-green deployment strategy Automatic rollback on health check failure Slack notification on success/failure to [channel] Infrastructure: Generate Terraform/Pulumi IaC for [AWS/GCP/Azure] resources Docs: Create DEPLOYMENT.md with architecture diagram and runbook Test the pipeline locally using act or equivalent tool before committing. Why This Is a Game-Changer: Los pipelines de DevOps suelen tardar días en configurarse correctamente con la verificación de seguridad adecuada y el rollback. Integrates security (shift-left) directly into the pipeline from day one Blue-green deployment and auto-rollback eliminate downtime risk Infrastructure-as-Code garantiza entornos reproducibles en todo el equipo ⏱ Time Saved: 20+ hours manually → 30 minutes with Antigravity Proyecto Cross-Browser UI Testing Suite - When you visit a website, it should look and work the same whether you're using Chrome, Firefox, Safari, or a phone — but it often doesn't. - UI testing means automatically checking every page, form, and button across different browsers and screen sizes to catch visual bugs. - This prompt tells Antigravity to set up a complete testing framework that verifies user flows, checks visual appearance, measures performance, and ensures accessibility compliance. - The result is a safety net that catches broken layouts and usability issues before real users encounter them. Google Antigravity Prompt: Create an end-to-end browser testing suite for the web application at [URL or local path]: Framework Setup: Initialize [Playwright/Cypress] with TypeScript support Test Scenarios: Write automated tests for: User registration and login flow Navigation through all main pages ([list pages]) Form validation (required fields, email format, password strength) Shopping cart / core workflow ([describe primary user journey]) Responsive design at 320px, 768px, 1024px, 1440px breakpoints Visual Regression: Capture baseline screenshots for all pages, configure visual diff threshold at 0.1% Performance: Add Lighthouse CI checks for Core Web Vitals (LCP < 2.5s, CLS < 0.1) Accessibility: Run axe-core accessibility audit, fail on WCAG 2.1 AA violations Reporting: Generate HTML test report with screenshots of failures CI Integration: Add test run to [CI platform] pipeline config Use Antigravity's browser subagent to manually verify each critical flow after test creation. Why This Is a Game-Changer: Comprehensive browser testing is the most neglected quality practice — this automates it completely Regresión visual captura brechas de CSS que las pruebas de unidad no pueden detectar Accessibility compliance (WCAG 2.1 AA) is legally required in many jurisdictions Antigravity's browser subagent can physically verify the tests work correctly ⏱ Time Saved: 30+ hours manually → 45 minutes with Antigravity Capítulo 5: Microservices Architecture Design & Scaffolding Los microservicios son una forma de construir aplicaciones grandes dividiéndolas en pequeños servicios independientes que cada uno maneja una tarea específica, como departamentos separados en una empresa. - This approach makes systems easier to scale, update, and maintain, but designing one from scratch is complex and time-consuming Este prompt le pide a Antigravity que diseñe toda la arquitectura, cree cada servicio, establezca la comunicación entre ellos, configure un gateway de API y orquestre todo con Docker. - El resultado es un sistema distribuido totalmente escalonado que normalmente requeriría semanas de planificación arquitectónica y instalación Google Antigravity Prompt: Design and scaffold a microservices architecture for [application description, e.g., food delivery platform]: Domain Analysis: Identify bounded contexts and create a domain model diagram in Mermaid Services: Scaffold these microservices in [language]: [Service 1, e.g., User Service] — auth, profiles [Service 2, e.g., Order Service] — order lifecycle [Service 3, e.g., Payment Service] — Stripe integration [Service 4, e.g., Notification Service] — email/SMS via [provider] Communication: Implement async messaging via [RabbitMQ/Kafka] with event schemas API Gateway: Configure [Kong/Traefik] with routing, rate limiting, and JWT validation Database: Each service gets its own [PostgreSQL/MongoDB] instance — no shared DBs Observability: Add structured logging, Prometheus metrics, and distributed tracing (Jaeger) Docker: Create docker-compose.yml orchestrating all services, databases, message broker, and gateway Place all services under [workspace path] with a shared proto/schema directory. Why This Is a Game-Changer: Las decisiones de arquitectura de microservicios (patrones de comunicación, base de datos por servicio) requieren semanas de reuniones de diseño Event-driven communication with proper schemas prevents integration nightmares La pila de observabilidad incorporada significa el desmantelamiento de la producción desde el primer día Docker compose enables local development of the entire system on a single machine ⏱ Time Saved: 60 hours manually → 2 hours with Antigravity La Sección Corporativa - In the corporate world, success depends on how quickly you can turn raw information into clear decisions, professional reports, and organized action plans. - These prompts are designed to handle the "knowledge management" burden that slows down every office, from drafting quarterly reports to tracking meeting outcomes across large teams. - Nos centramos en automatizar la inteligencia competitiva, simplificar la incorporación de nuevos empleados y llevar a cabo auditorías complejas de cumplimiento de políticas con investigación regulatoria en tiempo real. - Using these tools, a single employee can perform the work of an entire administrative department, ensuring that nothing is forgotten and every decision is backed by data. 6: Automated Business Report Generation - A quarterly business report transforms raw data — sales numbers, customer feedback, web analytics — into a polished document that executives use to make decisions - Crear uno manualmente significa horas de trabajo de hoja de cálculo, formatación de gráficos y escritura de insights - This prompt instructs Antigravity to read your data files, perform calculations, generate charts, write an executive summary with actionable recommendations, and produce a branded, navigable HTML report - El resultado es un informe listo para la presentación con insights orientados a datos que normalmente consumirían dos a tres días laborales completos de tiempo de analista. Google Antigravity Prompt: Generate a comprehensive quarterly business report from raw data: Data Sources: Read and analyze: Sales data from [file path, e.g., C:/Reports/Q4_sales.csv] Customer feedback from [file path, e.g., C:/Reports/feedback.xlsx] Web analytics from [URL: e.g., Google Analytics export] Analysis: Calculate and present: Revenue by product line, region, and channel (tables + charts) Month-over-month growth rates with trend indicators Customer satisfaction scores (NPS) with sentiment breakdown Top 5 performing and bottom 5 underperforming products Insights: Generate executive summary (300 words) highlighting: Key wins and areas of concern Actionable recommendations (minimum 5) Competitive benchmarking insights from [competitor URLs] Formatting: Output as polished Markdown + export-ready HTML with: Company branding colors ([hex codes]) Professional charts using Chart.js Table of contents with clickable navigation Save to [output path] as Q4_Report.html and Q4_Report.md Why This Is a Game-Changer: Quarterly reports consume 2–3 full workdays of analyst time per cycle Benchmarking competitivo automatizado a través del acceso web proporciona un contexto de mercado en tiempo real Consistent formatting eliminates back-and-forth with management on presentation quality Data-driven recommendations replace gut-feel analysis with quantified insights ⏱ Time Saved: 16+ hours manually → 20 minutes with Antigravity Prompt 7: Minutos de reunión y rastreo de elementos de acción - Después de cada reunión, alguien debe escribir lo que se discutió, lo que se decidió, y quién es responsable de lo que - y luego seguir adelante para asegurarse de que realmente se haga. - This administrative work is tedious but critical; without it, decisions are forgotten, and tasks fall through the cracks. - This prompt has Antigravity process a meeting transcript, extract structured minutes, create action item tables with owners and deadlines, draft follow-up emails, and generate calendar invites. - The result is a complete accountability system that turns every meeting into tracked, actionable outcomes. Google Antigravity Prompt: Process meeting recordings/transcripts and create structured documentation: Input: Read the meeting transcript from [file path, e.g., C:/Meetings/standup_2026-02-16.txt] Structure: Extract and organize into: Attendees: List all participants mentioned Agenda Items: Numbered list of topics discussed Key Decisions: Bulleted list with rationale for each decision Action Items: Table with columns: Task | Owner | Deadline | Priority (High/Med/Low) Open Questions: Unresolved items requiring follow-up Follow-up Email: Draft a professional follow-up email summarizing decisions and action items, addressed to [team email/distribution list] Tracking: Create/update ACTION_TRACKER.md at [path] — append new items, mark previously completed items Calendar: Generate .ics calendar invites for action items with deadlines Format: Use Markdown with emoji indicators (✅ done, 🔴 overdue, 🟡 in progress) Cross-reference with previous meeting notes at [directory] to identify recurring unresolved items. Why This Is a Game-Changer: Meeting follow-up is the #1 productivity black hole — action items get lost within hours Referencias cruzadas automáticas con reuniones anteriores superficies bloqueadores crónicos La generación de invitaciones de calendario asegura que los plazos sean visibles en el flujo de trabajo de todos Persistent action tracker creates organizational accountability over time ⏱ Time Saved: 3+ hours per meeting manually → 5 minutes with Antigravity 8: Competitive Intelligence Dashboard - Inteligencia competitiva significa investigar sistemáticamente a sus competidores - sus productos, precios, estrategias y posicionamiento de mercado - para informar sus propias decisiones de negocio. - Most companies either pay expensive consultants or spend weeks doing this manually across dozens of websites. - This prompt tells Antigravity to visit competitor websites, extract product and pricing data, analyze technology stacks, build comparison matrices, conduct SWOT analyses, and produce strategic recommendations. El resultado es un informe de inteligencia integral y basado en fuentes que permite la planificación estratégica basada en datos en una fracción del tiempo y el costo. Google Antigravity Prompt: Research competitors and build a competitive intelligence report: Competitors: Research these companies via their websites: [Competitor 1 URL] [Competitor 2 URL] [Competitor 3 URL] Data Collection: For each competitor, extract: Product/service offerings and pricing tiers Recent news, press releases, and blog announcements Technology stack (use [BuiltWith URL] or similar) Social media presence and engagement metrics from [social URLs] Analysis Framework: Create comparison matrices for: Feature parity (what they have that we don't, and vice versa) Pricing comparison (normalized per user/month) Market positioning (value vs. premium vs. budget) SWOT Analysis: Generate a SWOT table for our position relative to each competitor Recommendations: Provide 10 strategic recommendations prioritized by impact and effort Output: Save as Competitive_Intel_Feb2026.md at [output path] Include all source URLs as footnote references for verification. Why This Is a Game-Changer: Competitive analysis normally requires expensive subscriptions (Gartner, CB Insights) or weeks of manual research Antigravity's internet access enables real-time data collection from competitor websites Structured SWOT and feature matrices provide immediately actionable strategic inputs Footnoted sources ensure every data point is verifiable by leadership ⏱ Time Saved: 24+ hours manually → 30 minutes with Antigravity 9: Employee Onboarding Documentation System Cuando un nuevo empleado se une a una empresa, necesitan guías claras sobre la configuración de sus herramientas, la comprensión de la estructura del equipo, el aprendizaje de la terminología interna y saber a quién pedir ayuda. La mala documentación de embarque significa semanas de confusión y pérdida de productividad para cada nueva contratación. - This prompt asks Antigravity to analyze existing documentation, generate a day-by-day onboarding guide, create technical setup instructions with copy-pasteable commands, compile a FAQ from real questions, and build a glossary. - The result is a professional onboarding system that gets new employees productive in days instead of weeks. Google Antigravity Prompt: Create a comprehensive employee onboarding documentation system: Analyze: Read existing documentation at [docs directory path] and company wiki at [wiki URL] Onboarding Guide: Generate ONBOARDING.md covering: Day 1 checklist (accounts, tools, introductions) Week 1 learning path with links to internal resources 30-60-90 day milestones with measurable objectives Team structure diagram (Mermaid org chart) Key contacts table (Name | Role | Slack | Email) Technical Setup: Create DEV_SETUP.md with: Step-by-step environment setup for [OS: Windows/Mac/Linux] Required tools installation commands (copy-pasteable) Repository clone and configuration instructions Verification checklist (all green = ready to contribute) FAQ: Compile ONBOARDING_FAQ.md from common questions found in [Slack channel URL/export] Glossary: Create COMPANY_GLOSSARY.md of internal terminology Save all files to [output directory] Ensure all links are valid by checking each URL. Why This Is a Game-Changer: New hire onboarding typically takes 2–4 weeks to become productive; this cuts it to days Link validation ensures documentation stays fresh and doesn't point to dead pages Copy-pasteable setup commands eliminate the "it works on my machine" problem FAQ generation from real Slack questions addresses actual pain points, not assumed ones ⏱ Time Saved: 30+ hours manually → 45 minutes with Antigravity Prompt 10: Policy Compliance Audit & Gap Analysis - Compliance means ensuring your organization follows legally required standards like GDPR (data privacy), SOC2 (security), or HIPAA (healthcare data) — failure can result in massive fines. - Auditing your current policies against these standards is expensive, time-consuming, and requires specialized knowledge. - This prompt has Antigravity read your internal policies, research current regulatory requirements from official sources, identify gaps in a structured compliance matrix, assess risk levels, and generate a phased remediation roadmap. - The result is a comprehensive audit with a clear action plan that would normally cost tens of thousands in consultant fees. Google Antigravity Prompt: Perform a compliance audit of internal policies against regulatory standards: Input Documents: Read all policy files from [policies directory path] Regulatory Framework: Research current requirements for [standard: GDPR/SOC2/HIPAA/ISO 27001] from official sources: [Regulatory body URL, e.g., gdpr.eu] [Framework documentation URL] Gap Analysis: Create compliance matrix (table format): Requirement ID | Description | Current Status (✅ Compliant / ⚠️ Partial / ❌ Non-Compliant) | Evidence | Remediation Action Risk Assessment: For each gap: Severity (Critical/High/Medium/Low) Potential financial/legal impact Estimated remediation effort (hours) Priority ranking Remediation Plan: Generate COMPLIANCE_ROADMAP.md with: Phased timeline (30/60/90 days) Resource requirements per phase Template policy documents for critical gaps Executive Summary: One-page brief for C-suite with RAG status indicators Save all outputs to [compliance audit directory]. Why This Is a Game-Changer: Las auditorías de conformidad cuestan entre $50,000 y $200,000 con consultores externos, lo que proporciona un primer paso completo. Investigación regulatoria en tiempo real garantiza que la auditoría refleje los requisitos más recientes La hoja de ruta de recuperación gradual da a la gestión un plan de acción claro con necesidades de recursos Template policy documents accelerate gap closure instead of starting from scratch ⏱ Time Saved: 80+ hours (consultant engagement) → 2 hours with Antigravity La Sección Investigadora - Scientific research is a race between great ideas and the overwhelming volume of existing data that must be searched, cleaned, and analyzed to prove those ideas correct. - Estas promesas transforman las fases de "revisión literaria" y "análisis de datos" de la investigación de tareas manuales de meses a procesos rápidos, automatizados y estadísticamente rigurosos. - We provide workflows for conducting systematic reviews, building reproducible analysis pipelines, drafting grant proposals, and ensuring your experiments meet global standards. - Esta sección es para el científico que quiere pasar menos tiempo formateando citas y limpiando archivos CSV, y más tiempo descubriendo nuevos conocimientos e interpretando los resultados. 11: Automatización de revisión sistemática de la literatura - A systematic literature review involves searching thousands of academic papers to find, analyze, and synthesize everything written about a specific research topic - For a human researcher, this process typically takes three to six months of painstaking searching, reading, and organizing - This prompt directs Antigravity to search major academic databases, select papers based on strict criteria, extract key findings and methodologies, and generate a structured review with a gap analysis - The result is a comprehensive literature map and initial draft that accelerates the research process by months Google Antigravity Prompt: Conduct a systematic literature review on [research topic, e.g., "transformer architectures for time series forecasting"]: Search: Query these academic sources via web: [Google Scholar URL with search query] [arXiv URL with search filters] [Semantic Scholar URL] Selection Criteria: Include papers that: Published between [start year] and [end year] Minimum [N] citations (for older papers) or from top-tier venues Directly address [specific sub-topic or methodology] Extraction: For each paper, record: Title, Authors, Venue, Year, Citation Count Research question and hypothesis Methodology summary (3-4 sentences) Key findings and limitations Datasets and metrics used Synthesis: Create: Taxonomy of approaches (Mermaid diagram) Comparison table (Method | Dataset | Metric | Result) Research gap analysis identifying under-explored areas Recommended future research directions (minimum 5) Output: Save as Literature_Review_[topic].md at [path] Format all citations in [APA/IEEE/Chicago] style. Why This Is a Game-Changer: Systematic literature reviews take 3–6 months manually; this produces a comprehensive first draft in hours Web access enables real-time search across multiple academic databases simultaneously Structured extraction ensures no paper is reviewed superficially or inconsistently El análisis de la brecha acelera la transición de la revisión a la contribución de investigación original ⏱ Time Saved: 200 hours manually → 2 hours with Antigravity 12: Pipeline de análisis de datos de investigación El análisis de datos de investigación implica limpiar datos experimentales crudos, realizar pruebas estadísticas para ver si los resultados son significativos y crear gráficos claros para su publicación. - Doing this manually requires deep expertise in coding and statistics, and even small errors can lead to invalid scientific conclusions. Este prompt tiene Antigravity construye un tubo de análisis completo: trata los datos faltantes, ejecuta las pruebas estadísticas correctas basadas en la distribución de los datos, y produce visualizaciones de alta calidad. - El resultado es un análisis reproducible y estadísticamente riguroso que cumple con los más altos estándares para la publicación científica. Google Antigravity Prompt: Build a reproducible data analysis pipeline for my research dataset: Data Loading: Read dataset from [file path, e.g., C:/Research/experiment_data.csv] Preprocessing: Create Python script that: Handles missing values (strategy: [mean imputation/KNN/drop]) Detects and treats outliers using IQR method Normalizes features using [StandardScaler/MinMax] Encodes categorical variables (one-hot / label encoding) Exploratory Analysis: Generate: Descriptive statistics summary table Correlation heatmap (Seaborn, publication quality) Distribution plots for all numeric variables Class imbalance analysis (if classification task) Statistical Tests: Run appropriate tests: Normality test (Shapiro-Wilk) [t-test/ANOVA/chi-square/Mann-Whitney] for hypothesis testing Effect size calculations (Cohen's d / Cramér's V) p-value corrections for multiple comparisons (Bonferroni) Output: Save all plots as 300 DPI PNGs, results as analysis_results.md Reproducibility: Create requirements.txt and README.md with run instructions All code must be PEP 8 compliant with comprehensive docstrings. Why This Is a Game-Changer: Ensures statistical rigor (effect sizes, multiple comparison corrections) that many researchers omit Las visualizaciones de calidad de publicación eliminan las luchas de formatación manual de Matplotlib Full reproducibility package (requirements.txt, README) meets open science standards Automated normality testing selects the correct statistical test, preventing methodological errors ⏱ Time Saved: 25 hours manually → 30 minutes with Antigravity 13: Grant Proposal Draft Generator - A research grant proposal is a detailed document explaining why a project deserves funding, how it will be conducted, how much it will cost, and what its impact will be. - Writing a winning proposal is highly competitive and often consumes up to 40% of a researcher's working time. - This prompt instructs Antigravity to research specific funder requirements, align the project goals with those priorities, draft all required sections, create a timeline, and justify the budget. - El resultado es un proyecto de propuesta estratégicamente alineado que maximiza las posibilidades de asegurar la financiación de la investigación crítica. Google Antigravity Prompt: Draft a research grant proposal for [funding body, e.g., NSF/ERC/NIH]: Research: Visit the funding body website at [URL] to extract: Current funding priorities and themes Eligibility criteria and deadlines Evaluation criteria and scoring rubric Maximum budget and duration Proposal Sections: Generate drafts for: Abstract (250 words): Concise problem-solution-impact summary Introduction: Background, gap in knowledge, research questions Literature Context: Key references supporting the proposal (cite 15–20 works) Methodology: Detailed research design, data collection, analysis plan Timeline: Gantt chart (Mermaid) with milestones across [duration] Budget Justification: Itemized budget table with line-by-line rationale Impact Statement: Societal relevance, knowledge contribution, dissemination plan Alignment: Map each section to the funder's evaluation criteria Formatting: Follow [funder's template/format guidelines] Save to [output path] as Grant_Proposal_[funder]_[topic].md Research topic: [your research topic and objectives]. Why This Is a Game-Changer: Grant writing consumes 20–40% of a researcher's time with success rates of only 15–25% Real-time access to funder websites ensures alignment with the latest priorities and criteria Evaluation criteria mapping maximizes scoring potential on every section Las plantillas de justificación presupuestaria impiden la razón #1 para el rechazo administrativo ⏱ Time Saved: 60 hours manually → 2 hours with Antigravity 14: Experiment Reproducibility Package - Reproducibility is the cornerstone of science; it means another researcher should be able to run your experiment and get the exact same results. Muchos proyectos de investigación fallan en esta prueba debido a la falta de dependencias, configuraciones de código duro o pasos de procesamiento de datos no documentados. - This prompt tells Antigravity to audit your experiment code, create a standardized environment (Docker), automate the data processing, and provide a one-command reproduction script. El resultado es un paquete "preparado para la ciencia" que asegura que su trabajo sea confiable, verificado y puede ser construido por la comunidad científica global. Google Antigravity Prompt: Create a complete reproducibility package for my ML experiment: Code Audit: Review the experiment code at [repo/directory path] for: Hardcoded random seeds (ensure all are set and documented) Non-deterministic operations (flag and fix or document) Missing dependency version pins Environment: Generate: environment.yml (Conda) with exact version pins Dockerfile for containerized reproduction Makefile with targets: setup, train, evaluate, visualize Data Pipeline: Create: data/README.md with dataset source, license, and download instructions Checksum verification script for data integrity Preprocessing script that transforms raw → processed data deterministically Experiment Config: Create configs/ directory with: Hyperparameter YAML files for each experiment variant Ablation study configurations Results Verification: Script that re-runs experiment and compares outputs against stored baseline results (tolerance: [threshold]) Documentation: REPRODUCE.md with one-command reproduction instructions Verify the package by running the full pipeline end-to-end. Why This Is a Game-Changer: The reproducibility crisis affects 70%+ of ML research — this solves it systematically La containerización de Docker elimina el “infierno de la dependencia” en diferentes máquinas y versiones de sistema operativo Checksum verification ensures data integrity — a step most researchers skip entirely One-command reproduction (via Makefile) lets reviewers validate results in minutes, not days ⏱ Time Saved: 20 hours manually → 1 hour with Antigravity 15: Proyecto de documento de investigación y formatación - Writing a research paper requires following incredibly strict formatting rules that differ for every scientific journal and conference. Un artículo puede ser rechazado simplemente por tener el tamaño de la fuente incorrecto, el estilo de la cita o el diseño de la página, incluso si la investigación en sí es revolucionaria. - This prompt asks Antigravity to research specific venue guidelines, organize your notes and results into a structured draft, and apply the exact 100% required formatting. - The result is a professional paper draft that is ready for submission, preventing "desk rejections" and letting you focus on the science rather than the margins. Google Antigravity Prompt: Draft a research paper following [venue: e.g., IEEE/ACM/Nature] formatting guidelines: Template: Research the submission guidelines at [conference/journal URL] and create LaTeX/Markdown template matching requirements Content Input: Read: Experiment results from [results directory path] Literature review from [literature review file path] Methodology notes from [notes file path] Sections: Draft: Title: 3 alternative titles optimized for discoverability Abstract: Structured (Background-Methods-Results-Conclusion), 250 words Introduction: Problem motivation → gap → contribution → paper structure Related Work: Organized by theme, positioning our contribution clearly Methodology: Formal description with equations and algorithm pseudocode Results: Tables and figure captions for all experiment outcomes Discussion: Findings interpretation, limitations, and threats to validity Conclusion: Key contributions, implications, future work References: Format all citations in [required style] with DOI links Checklist: Create submission checklist from venue guidelines Save to [output path] as paper_draft_v1.md. Why This Is a Game-Changer: Paper writing takes 2–6 months; Antigravity generates a structured first draft in hours Venue-specific formatting compliance prevents desk rejection (which wastes months of review time) Structured abstract and clear contribution positioning improve acceptance odds significantly Lista de verificación de envío garantiza que no se pierdan materiales adicionales o requisitos de formatación ⏱ Time Saved: 100 hours manually → 2 hours with Antigravity The Student Section - Education is not just about what you know, but how effectively you can manage your time, organize your study materials, and prepare for high-stakes assessments. - These prompts act as a private AI tutor and project manager, helping you build personalized study plans based on your own weak areas and scientifically proven memorization techniques. - We cover the entire academic journey, from structuring a 100-page thesis and building interactive tutorials to generating realistic practice exams and coordinating complex group projects. Ya sea que sea un graduado o un doctorado, estas herramientas aseguran que gasta su energía en el aprendizaje profundo en lugar de la frustración logística. 16: Generador de plan de estudio personalizado - A personalized study plan is a roadmap that breaks down a complex subject into manageable daily tasks, focusing on exactly what you need to learn most. La mayoría de los estudiantes estudian "linealmente", lo que es ineficiente; la evidencia científica muestra que la "repetición espaciada" (revisión a intervalos específicos) es 3 veces más efectiva. - This prompt has Antigravity analyze your syllabus, identify your weak areas, and generate a customized schedule with built-in review sessions and curated free learning resources. - El resultado es un sistema de estudio científicamente optimizado que maximiza su memoria y minimiza el tiempo perdido. Google Antigravity Prompt: Create a comprehensive, personalized study plan for [subject/course, e.g., Data Structures and Algorithms]: Syllabus Analysis: Read the course syllabus from [file path or URL] Assessment: Based on my self-assessment ([list weak topics, e.g., graph algorithms, dynamic programming]), prioritize topics accordingly Schedule: Generate a [N-week] study plan with: Daily study sessions (2-hour blocks) Spaced repetition review days (Days 3, 7, 14, 30 after initial study) Practice problem sets (5 easy → 5 medium → 3 hard per topic) Weekly mock test dates Resources: Research and link to: Free online courses ([platforms: Coursera/MIT OCW/Khan Academy]) YouTube tutorials for each difficult topic Practice platforms ([LeetCode/HackerRank] problem links by topic) Textbook chapter references from [textbook name] Tracking: Create STUDY_TRACKER.md with checkboxes for each session Flashcards: Generate Anki-compatible flashcards (Q&A format) for key concepts Save all files to [study directory path]. Why This Is a Game-Changer: Personalized plans based on weak areas are 3x more effective than generic syllabi Spaced repetition scheduling is scientifically proven but rarely implemented manually Recursos gratuitos curados (via búsqueda web) eliminan horas de búsqueda de materiales de calidad Las tarjetas flash de Anki permiten el estudio móvil y aprovechan la técnica de memorización más efectiva ⏱ Time Saved: 8 hours planning manually → 10 minutes with Antigravity 17: Thesis/Dissertation Structure & Literature Mapping - Una tesis o disertación es el proyecto más grande que la mayoría de los estudiantes jamás llevarán, y comenzar puede ser paralizante debido al gran volumen de investigación y escritura necesario. Un "mapa de la literatura" es una forma visual de ver cómo diferentes trabajos de investigación se conectan, ayudándote a encontrar tu propio "gap" único para escribir. - Esta guía instruye a Antigravidad a refinar sus preguntas de investigación, mapear los trabajos seminal en su campo, y crear un resumen detallado capítulo por capítulo con cuentas de palabras. - El resultado es un marco estructural concreto que convierte un proyecto intimidante de 100 páginas en una serie de hitos logrados. Google Antigravity Prompt: Help me structure my thesis on [topic, e.g., "Impact of Social Media on Political Polarization"]: Research Question Refinement: Based on my initial idea, suggest: 1 primary research question (specific, measurable) 3 secondary research questions Null and alternative hypotheses Literature Mapping: Search [Google Scholar/JSTOR/PubMed URL] for: 20 seminal papers in the field (sorted by citation count) 10 recent papers (last 2 years) showing current trends 5 contrarian/critical papers offering opposing viewpoints Create a literature concept map (Mermaid diagram) Thesis Structure: Generate chapter outline: Chapter titles with 3-5 sub-section headings each Estimated word count per chapter (totaling [target word count]) Key arguments to develop in each chapter Methodology Planning: Recommend: Research design (qualitative/quantitative/mixed) Data collection methods appropriate for my research questions Analysis framework with justification Timeline: Create a Gantt chart for [duration: e.g., 12 months] Save as THESIS_PLAN.md at [output path]. Why This Is a Game-Changer: Thesis planning is the most anxiety-inducing phase — this provides a concrete, actionable structure El mapeo de la literatura en múltiples bases de datos superficializa los documentos que las búsquedas de palabras clave pierden Including contrarian papers demonstrates critical thinking (a key examiner criterion) La asignación del número de palabras evita la trampa común de los métodos de sobreescribir y el análisis de subescribir ⏱ Time Saved: 40 hours manually → 1 hour with Antigravity 18: Interactive Coding Tutorial Builder Aprender a codificar se hace mejor a través de la práctica, pero encontrar tutoriales que no sean ni demasiado fáciles ni demasiado difíciles es difícil. - An "interactive" tutorial includes explanation, code examples, and exercises that are automatically checked for correctness. - This prompt has Antigravity build a customized 10-lesson coding course for you, complete with working examples, guided exercises, and "auto-graders" that test your code instantly. El resultado es una clase de codificación privada que captura tus errores y proporciona retroalimentación inmediata al igual que un tutor humano. Google Antigravity Prompt: Create an interactive coding tutorial for [programming topic, e.g., "Python Object-Oriented Programming"]: Structure: Design 10 progressive lessons: Each lesson: Concept explanation (200 words) → Code example → Exercise → Solution Difficulty progression: Beginner (1-3) → Intermediate (4-7) → Advanced (8-10) Code Examples: For each lesson, create: Working demonstration code in [language] Annotated comments explaining every non-obvious line Common mistake examples with explanations of why they fail Exercises: Generate per lesson: 1 guided exercise (with hints) 1 independent exercise (solution hidden) 1 challenge problem connecting to real-world applications Auto-Grading: Write unit test files for each exercise that students can run to verify their solutions Cheat Sheet: Create a one-page reference card for all concepts covered Prerequisites: List required knowledge with links to prerequisite tutorials Save complete tutorial to [tutorial directory path] with clear folder structure: lesson-01/, lesson-02/, etc., each containing README.md, code files, and test files. Why This Is a Game-Changer: Self-paced tutorials with auto-grading provide instant feedback — no waiting for TAs Dificultad progresiva con contextos del mundo real mantiene la motivación y demuestra relevancia Ejemplos de errores comunes enseñan habilidades de depuración, no solo patrones correctos La verificación de pruebas de unidad enseña el desarrollo impulsado por pruebas (TDD) como una habilidad bonus ⏱ Time Saved: 15+ hours creating manually → 20 minutes with Antigravity 19: Generador de pruebas de preparación y práctica de exámenes Las pruebas de práctica son la forma más efectiva de prepararse para los exámenes porque te obligan a recordar información en lugar de simplemente volver a leer notas. - Creating your own practice questions is time-consuming and often lacks the difficulty calibration of a real exam. - This prompt tells Antigravity to read your syllabus, research past exam papers from your university, and generate three mock exams of increasing difficulty with full model answers. - El resultado es un "terreno de entrenamiento" de alta presión que asegura que nunca se sorprenda con las preguntas del día del examen. Google Antigravity Prompt: Generate comprehensive exam preparation materials for [subject, e.g., "Operating Systems"]: Syllabus: Read the exam syllabus/topics from [file path or URL] Study Notes: For each topic, create concise revision notes: Key definitions and concepts (bullet points) Important formulas/algorithms with worked examples Comparison tables (e.g., process vs thread, paging vs segmentation) Mnemonic devices for memorization-heavy topics Practice Tests: Generate 3 mock exams: Mock 1: 30 MCQs covering all topics (with answer key and explanations) Mock 2: 10 short-answer questions (model answers provided) Mock 3: 5 long-form essay/problem-solving questions mimicking real exam format Difficulty Calibration: Research past exam papers from [university/course URL] to match difficulty level Weak Area Drill: Extra 20 practice questions targeting my weak topics: [list topics] Quick Reference: Create a printable 2-page cheat sheet (if open-book) or key-term glossary Save to [exam prep directory] organized by topic. Why This Is a Game-Changer: Custom practice tests calibrated to specific exams are 5x more effective than generic question banks Investigar artículos pasados a través de la web asegura que el nivel de dificultad coincida con el examen real La perforación dirigida a áreas débiles maximiza la eficiencia del estudio utilizando el principio de Pareto (regla 80/20) Formatos de preguntas múltiples (MCQ, respuesta corta, ensayo) para preparar cualquier estructura de examen ⏱ Time Saved: 12+ hours manually → 15 minutes with Antigravity 20: Group Project Coordination Hub Los proyectos de grupo a menudo fallan no por mal código o escritura, sino por mala comunicación: alguien pierde una fecha límite, o dos personas hacen la misma tarea dos veces. Un hub de coordinación es un conjunto central de documentos y plantillas que asegura que cada miembro del equipo sabe exactamente de qué es responsable. Esta prompt pide a Antigravity que descomponga su proyecto en tareas, asigne las tareas de forma justa, crea una línea de tiempo compartida (grafito de Gantt) y establezca plantillas de informe estandarizadas. - El resultado es un "gerente de proyecto automatizado" que mantiene el equipo organizado, responsable y enfocado en la rubrica de clasificación. Google Antigravity Prompt: Set up a complete coordination system for our group project on [project topic]: Project Charter: Create PROJECT_CHARTER.md with: Project title, objective, and scope Team members: [names] with assigned roles Communication plan (meeting schedule, platform: [Slack/Discord/WhatsApp]) Grading criteria from [rubric file/URL] Task Breakdown: Create TASKS.md with: Work Breakdown Structure (WBS) in tree format Task assignments (balanced workload per member) Dependencies between tasks (Mermaid Gantt chart) Due dates aligned with submission deadline: [date] Templates: Generate standardized templates for: Individual progress report (weekly) Meeting minutes (with action items table) Peer evaluation form Repository Structure: Initialize project folder at [path] with: Organized directories: docs/, src/, data/, presentations/ .gitignore for [language/framework] README.md with project overview and setup instructions Integration Checklist: Create INTEGRATION.md for combining individual contributions Ensure workload is equitably distributed across [N] team members. Why This Is a Game-Changer: Los proyectos de grupo a menudo fallan debido a la coordinación, no a la capacidad. Gantt chart with dependencies prevents bottlenecks where one member blocks another Standardized templates ensure consistent quality across all members' contributions Peer evaluation forms built in from the start encourage accountability throughout ⏱ Time Saved: 10+ hours manually → 15 minutes with Antigravity La Sección de Ingenieros Para aquellos que construyen la próxima generación de tecnología de IA, el desafío ya no es simplemente “hacer que funcione”, sino hacerla rápida, segura, rentable y precisa. Estas prompts proporcionan una configuración de nivel industrial para los flujos de trabajo de IA más avanzados de hoy en día: evaluación de RAG, orquestación multiagente y ajuste del modelo localizado. Nos centramos en el lado "ingeniería" de la IA: la construcción de pilas de monitoreo, la realización de criterios de modelo rigurosos y el despliegue de sistemas que pueden escalar a miles de usuarios. Esta sección es para el ingeniero profesional de IA que necesita ir más allá de los "chatbots" y construir sistemas autónomos robustos y verificables para entornos empresariales. Prompt 21: Marco de Benchmarking y Comparación de Modelos ML Benchmarking significa ejecutar "testas" idénticas en diferentes modelos de IA (como Gemini, Claude y GPT) para ver cuál es realmente más inteligente, más rápido o más barato para una tarea específica. Con decenas de nuevos modelos lanzados cada mes, las empresas a menudo desperdician miles de dólares usando modelos que son "overkill" o simplemente subperforming para sus necesidades. Antigravidad construye un marco completo que prueba modelos en el razonamiento, la codificación y la seguridad, mientras que rastrea el coste exacto y la velocidad de cada respuesta. El resultado es una recomendación basada en datos que asegura que elijas el "cerebro" de IA correcto para tu proyecto sin adivinar. Google Antigravity Prompt: Build a comprehensive ML model benchmarking framework: Models: Set up comparison for [model list, e.g., GPT-4, Claude 3.5, Gemini 2.0, Llama 3.1, Mistral Large] Benchmark Suite: Implement evaluations for: Reasoning: GSM8K, ARC-Challenge, HellaSwag Coding: HumanEval, MBPP, SWE-bench Knowledge: MMLU, TruthfulQA Safety: ToxiGen, BBQ bias benchmark Infrastructure: Create Python framework with: Unified API interface (adapter pattern) for all model providers Async batch inference for throughput measurement Token-level cost calculation per benchmark Latency tracking (TTFT, tokens/sec) Analysis: Generate: Radar charts comparing models across dimensions Cost-performance Pareto frontier visualization Statistical significance tests (paired t-test) between model pairs Confidence intervals for all metrics Report: Create BENCHMARK_REPORT.md with publication-quality tables and charts Reproducibility: Include all configs, seeds, and exact prompts used Run against [API keys config path] and save results to [output directory]. Why This Is a Game-Changer: Model selection decisions cost companies $10K–$100K in wrong API spend — benchmarking prevents this El análisis de Pareto identifica el punto dulce entre calidad y presupuesto La prueba de significatividad estadística evita elegir modelos basados en el ruido en lugar de las diferencias reales El patrón de adaptador unificado hace que el marco sea extensible a medida que los nuevos modelos se lanzan mensualmente ⏱ Time Saved: 50+ hours manually → 2 hours with Antigravity 22: Gasoducto RAG con Harness de Evaluación - RAG (Retrieval-Augmented Generation) es una técnica que da a la IA una "biblioteca privada" (sus documentos) para leer de modo que pueda responder a las preguntas con precisión sin hacer las cosas. - A major problem with RAG is "hallucination"—when the AI sounds confident but is actually wrong—and evaluating how often this happens is notoriously difficult. - This prompt instructs Antigravity to build a production-grade RAG system that includes an "evaluation harness" to quantitatively measure its accuracy and faithfulness. - The result is a trustworthy AI assistant that cites its sources and admits when it doesn't know the answer, backed by hard performance metrics. Google Antigravity Prompt: Build a production-grade Retrieval-Augmented Generation (RAG) pipeline with evaluation: Data Ingestion: Process documents from [data directory path]: Support PDF, DOCX, Markdown, HTML formats Chunking strategy: recursive character splitting (chunk size: 512, overlap: 50) Extract metadata (source, page, section headings) Vector Store: Set up [ChromaDB/Pinecone/Weaviate] with: Embedding model: [text-embedding-3-large / all-MiniLM-L6-v2] Hybrid search (dense + sparse BM25) with reciprocal rank fusion Metadata filtering capabilities Retrieval: Implement: Query expansion (HyDE: Hypothetical Document Embeddings) Re-ranking with cross-encoder ([model name]) Context window optimization (stuff vs. map-reduce strategies) Generation: Configure [LLM] with custom system prompt enforcing: Source attribution (cite chunk IDs) Hallucination guardrails ("I don't know" when confidence < threshold) Evaluation: Implement RAGAS metrics: Faithfulness, Answer Relevancy, Context Precision, Context Recall Create 50 golden Q&A pairs for evaluation API: Wrap in FastAPI with streaming response support Save to [project directory]. Why This Is a Game-Changer: Los pipelines de RAG son el caso de uso de IA empresarial número uno, pero la mayoría de las implementaciones carecen de una evaluación adecuada Hybrid search (dense + BM25) with re-ranking outperforms simple vector search by 15–30% El marco de evaluación de RAGAS proporciona métricas cuantitativas de calidad en lugar de pruebas basadas en "vibes" Hallucination guardrails with source attribution are essential for enterprise trust and compliance Más de 40 horas manualmente → 2 horas con Antigravidad ⏱ Time Saved: 23: Pipeline de ajuste fino con seguimiento de experimentos Fine-tuning es como "enviar una IA a una escuela especializada" para que aprenda a hablar en un estilo específico, dominar un conjunto de datos único o seguir reglas complejas de formatación. - This process used to require massive supercomputers, but modern techniques now allow us to do it on a single high-end consumer computer. - This prompt asks Antigravity to set up a complete fine-tuning pipeline that cleans your data, trains the model efficiently, and logs every experiment so you can find the "best" version. El resultado es una IA entrenada a medida que entiende profundamente su dominio específico, listo para ser desplegado localmente a una fracción del coste de los grandes modelos de nube. Google Antigravity Prompt: Create an end-to-end LLM fine-tuning pipeline: Data Preparation: Process training data from [data file/directory]: Convert to instruction-following format (system/user/assistant) Train/validation/test split (80/10/10) with stratification Data quality checks (deduplication, length filtering, toxicity screening) Base Model: Set up [model: Llama 3.1 8B / Mistral 7B / Phi-3] with: QLoRA configuration (rank: 16, alpha: 32, target modules: all linear) 4-bit quantization (NF4) for memory efficiency Gradient checkpointing enabled Training: Configure: Learning rate: 2e-4 with cosine scheduler and 100-step warmup Batch size optimization (gradient accumulation for effective batch 32) Early stopping on validation loss (patience: 3 epochs) Experiment Tracking: Integrate [Weights & Biases / MLflow]: Log all hyperparameters, metrics, and system utilization Save checkpoints every [N] steps Evaluation: Benchmark against base model on [task-specific metrics] Export: Save merged model in GGUF format for local inference Documentation: Create TRAINING_REPORT.md with results and recommendations All code at [project path], GPU environment: [GPU type and count]. Why This Is a Game-Changer: Los tuberías de ajuste fino tienen más de 50 hiperparámetros, lo que configura los estándares óptimos para cada QLoRA + cuantización de 4 bits permite ajustar finamente los modelos 7B en una única GPU de consumo El seguimiento de experimentos integrado evita el problema de "¿cuál fue el buen?". GGUF export enables local deployment without GPU, dramatically reducing inference costs ⏱ Time Saved: 30+ hours manually → 1 hour with Antigravity 24: Sistema de agentes de IA con orquestación multi-herramienta - AI agents are programs that can "use tools"—they don't just chat; they can search the web, send emails, query databases, and perform actions autonomously. La "orquestación multi-herramienta" es la forma más avanzada de IA hoy en día, donde una IA "Supervisor" gestiona varias IA especializadas "Worker" para resolver problemas complejos y multi-pasos. Esta prompt le dice a Antigravidad que construya un sofisticado sistema de agentes con guardias seguras, roles especializados y puntos de control humanos para tomar decisiones sensibles. El resultado es una fuerza de trabajo digital autónoma que puede manejar procesos empresariales completos (como el soporte al cliente o la investigación de mercado) desde el final hasta el final. Google Antigravity Prompt: Build a multi-agent AI system for [use case, e.g., automated customer support]: Architecture: Design using [LangGraph/CrewAI/AutoGen] with: Supervisor agent (task routing and quality control) Specialist agents: [list 3-5 agent types, e.g., FAQ Agent, Escalation Agent, Analytics Agent] Shared memory (conversation history + knowledge base) Tools: Implement tool integrations: Database query tool ([PostgreSQL/MongoDB] connection) API caller tool (REST endpoints for [external services]) Email sender tool (SMTP configuration) Web search tool (Tavily/Serper API) Orchestration Logic: State machine defining agent transitions (Mermaid diagram) Fallback/escalation paths when confidence < [threshold] Human-in-the-loop breakpoints for sensitive actions Safety: Implement: Input validation and prompt injection detection Output guardrails (PII redaction, toxicity filtering) Rate limiting per user Testing: Create 30 test scenarios covering happy paths and edge cases Deployment: Dockerize with health checks and Prometheus metrics Save entire project to [directory]. Why This Is a Game-Changer: Los sistemas multiagentes son la frontera de la ingeniería de la IA – esto proporciona un punto de partida listo para la producción Supervisor pattern with specialist agents matches enterprise organization structures naturally Los puntos de interrupción humanos en el ciclo cumplen con los requisitos de cumplimiento para operaciones sensibles Detección de inyección rápida y redacción de PII abordan las dos principales preocupaciones de seguridad de la IA empresarial ⏱ Time Saved: 60+ hours manually → 2 hours with Antigravity 25: Model Deployment & Monitoring Stack La implementación es el acto de sacar un modelo de IA del "laboratorio" y ponerlo en un entorno de "producción" donde puede servir a los usuarios reales 24/7. El monitoreo es el sistema de seguridad que vigila las desaceleraciones, errores o "drifts" en la precisión que ocurren después de que un modelo haya estado en funcionamiento durante mucho tiempo. Antigravidad construye una infraestructura de alojamiento profesional con dashboards en tiempo real que te alertan si el sistema se vuelve lento o caro. El resultado es un servicio de IA robusto y listo para la producción que permanece saludable, rápido y rentable bajo la presión del uso del mundo real. Google Antigravity Prompt: Create a production model serving and monitoring infrastructure: Model Server: Deploy [model name] using [vLLM/TGI/Triton]: Quantization: [AWQ/GPTQ/FP8] Continuous batching with max batch size [N] Tensor parallelism across [N] GPUs OpenAI-compatible API endpoint Load Balancing: Configure [Nginx/Traefik] with: Health check endpoints Request queuing with priority levels Auto-scaling rules (CPU/GPU utilization thresholds) Monitoring Dashboard: Create Grafana dashboard tracking: Request latency (p50, p95, p99) Token throughput (tokens/sec) GPU memory and utilization Error rates and status codes Queue depth and wait times Alerting: Configure PagerDuty/Slack alerts for: Latency p99 > *[threshold]*ms Error rate > [threshold]% GPU memory > 90% A/B Testing: Implement traffic splitting between model versions Infrastructure: Full Kubernetes manifests or docker-compose.yml Save to [infrastructure directory path]. Why This Is a Game-Changer: Model deployment is where most ML projects die — this bridges the gap from notebook to production El batch continuo + la cuantificación puede reducir los costes de servicio en un 60-80% en comparación con la implementación ingenua El monitoreo de Grafana con alerta evita fallos silenciosos costosos en la producción A/B testing infrastructure enables safe, data-driven model upgrades without full rollouts Time Saved: 50+ hours manually → 2 hours with Antigravity The Technical Content Creator Section Crear contenido técnico de alta calidad -blogs, videos, boletines informativos o documentación- es una tarea masiva que requiere la escritura, la codificación y la producción simultáneamente. Estos prompts están construidos para eliminar el riesgo de "código roto" en los tutoriales mientras automatiza las partes repetitivas de scripting, creación de repo y investigación de SEO. - We provide complete kits for building documentation sites, scripting YouTube tutorials with timestamped branches, and designing multi-part email courses with landing pages. - For the modern creator, this means a 70% reduction in production time while delivering a significantly more professional and polished experience to your audience. 26: Post de blog técnico con ejemplos de código de trabajo - Technical blogging means explaining complex technologies to other developers through writing and—most importantly—code examples that actually work. Una gran frustración para los lectores es el "código roto", donde un blogger coloca un fragmento que no ha sido probado y no corre, destruyendo su credibilidad. - This prompt directs Antigravity to research the latest best practices, write a professional-quality tutorial, and physically test every single line of code locally before publishing. - The result is a high-authority blog post with a complete, verified project that readers can trust and use immediately. Google Antigravity Prompt: Write a production-quality technical blog post about [topic, e.g., "Building a Real-Time Chat App with WebSockets"]: Research: Search the web for: Latest best practices and official documentation for [technology] Common pitfalls developers face (Stack Overflow, Reddit, GitHub Issues) Competing approaches and when to use each Structure: Create the post with: Hook (2-3 sentences): Problem statement that resonates with developers Prerequisites: Tools, versions, and prior knowledge needed Step-by-step tutorial (5-8 sections): Each section with explanation → code block → expected output Troubleshooting FAQ: 5 common errors with solutions Conclusion: Key takeaways + links to further reading Code: Write all code examples in [language/framework]: Every snippet must be copy-pasteable and runnable Include inline comments for non-obvious logic Build a complete working project, not isolated fragments Test all code by running it locally before including SEO: Optimize with target keyword [keyword], meta description, and 5 internal link suggestions Save post to [output path] and working code to [code directory] Tone: conversational but technically precise. Target audience: [junior/mid/senior] developers. Why This Is a Game-Changer: Posts de blog técnicos con ejemplos de código roto destruyen la credibilidad del creador - Antigravidad prueba cada fragmento localmente La investigación web en tiempo real asegura que el post refleje las últimas versiones de las API y las mejores prácticas, no patrones desactualizados La optimización de SEO integrada en el proceso de escritura elimina el paso de investigación de palabras clave separado El proyecto de trabajo completo (no fragmentos) significa que los lectores pueden clonar y ejecutar de inmediato, aumentando el compromiso y las acciones. ⏱ Time Saved: 12 hours manually → 25 minutes with Antigravity 27: YouTube Tutorial Script with Timestamps & Repo Educar a las personas a través del vídeo requiere más que sólo grabar la pantalla; necesita un guión listo para la cámara, una estructura clara y un código organizado para que los espectadores sigan. - Un "repo acompañante" es una carpeta de código que coincide con diferentes etapas del vídeo, permitiendo a los espectadores saltar en cualquier momento sin perderse. Este prompt tiene el script de Antigravity para todo tu vídeo con pistas de pantalla, genera descripciones optimizadas para SEO con timestamps y construye un repositorio ramificado que coincida con la línea de tiempo del vídeo. - The result is a professional "creator's kit" that reduces production time by 60% while providing a much smoother learning experience for your audience. Google Antigravity Prompt: Create a complete YouTube tutorial package for [topic, e.g., "Deploy a FastAPI App to AWS Lambda in 10 Minutes"]: Research: Check YouTube for existing tutorials on this topic via [YouTube search URL] — identify gaps and unique angles Script: Write a spoken-word script (target: [N minutes]) with: Hook (0:00–0:30): "Have you ever struggled with…" + what they'll learn Overview (0:30–1:30): Architecture diagram description (Mermaid) + tools needed Step-by-step coding sections: Each section labeled with timestamp markers: [TIMESTAMP: 01:30 — Project Setup] [SCREEN: show terminal] / [SCREEN: show browser] cues Exact words to say while typing each code block Demo (final 2 min): Show the working application live CTA (last 30 sec): Subscribe + link to repo + next video suggestion Companion Repo: Create complete project at [repo path] with: Branches matching video sections (step-1-setup, step-2-api, final) README.md linking to the video with timestamps Description & Tags: Generate YouTube description with timestamps, 30 SEO tags, and 3 thumbnail title options Save script to [output path] Why This Is a Game-Changer: Los creadores de tutoriales gastan el 60% del tiempo de producción en el scripting, lo que genera un script listo para la cámara con señales de pantalla El reposo de compañero ramificado permite a los espectadores saltar a cualquier sección, reduciendo la frustración y las quejas de comentarios La descripción de YouTube con timestamps mejora las métricas del tiempo de observación y la recomendación algorítmica El análisis de la brecha competitiva asegura que el tutorial ofrece un ángulo único, no un reencuentro de contenido existente ⏱ Time Saved: 15 hours manually → 30 minutes with Antigravity 28: Documentación técnica Generador de sitios - Documentation is the user manual for software; without good documentation, even the most powerful tools are impossible to use and eventually die. Crear un moderno "sitio de documentos" implica leer miles de líneas de código para explicar cómo funciona cada función, crear guías y asegurarse de que todos los enlaces sean válidos. Esta prompt instruye a Antigravity a analizar toda su base de código, a generar automáticamente un sitio de documentación profesional (como Docusaurus), e incluso a incluir "cajas de arena de código" interactivas para que los usuarios puedan experimentar. El resultado es una experiencia de documentación "estándar de oro" que hace que su software se vea profesional y fácil de adoptar. Google Antigravity Prompt: Generate a complete documentation site for [project/library name] from source code: Code Analysis: Read the entire codebase at [repo path] and extract: All public APIs, classes, functions with signatures and docstrings Configuration options and environment variables Data models and their relationships Documentation Structure: Create using [Docusaurus/MkDocs/VitePress]: Getting Started: Installation, quick start (3 commands to "Hello World"), system requirements Guides: 5 task-oriented tutorials for common use cases of [project] API Reference: Auto-generated from code with parameter types, return values, examples Configuration: Every option documented with default value, type, and usage example FAQ: 10 questions derived from [GitHub Issues URL] and README Migration Guide: Breaking changes between versions (if applicable) Enhancements: Add copy buttons to all code blocks Include runnable code sandboxes (CodeSandbox/StackBlitz links) for key examples Search functionality (Algolia DocSearch or built-in) Verify: Build the site locally and check for broken links Save to [docs output directory] Write documentation for [target audience: beginners/intermediate/advanced]. Why This Is a Game-Changer: Los proyectos de código abierto viven o mueren por la calidad de la documentación: esto genera un sitio de documentos profesionales a partir de código Real GitHub Issues investiga las preguntas reales que los usuarios preguntan, no lo que los mantenedores asumen que preguntan Las cajas de arena de código ejecutables permiten a los usuarios experimentar sin instalación local, reduciendo drásticamente la fricción de la contribución Verificación automática de enlaces rotos evita el fallo de mantenimiento de la documentación #1 (enlaces muertos después de los refactores) ⏱ Time Saved: 40 hours manually → 2 hours with Antigravity 29: Newsletter Técnico / Constructor de cursos de correo electrónico Los cursos de correo electrónico son altamente eficaces porque enseñan a las personas temas complejos en pedazos de tamaño de picadura durante varios días, evitando que se sientan abrumados. Diseñar un buen curso requiere equilibrar la teoría con las tareas prácticas y escribir líneas de tema atractivas para que las personas realmente abran los correos electrónicos. Esta prompt pide a Antigravity que diseñe un currículo de varios días, escriba cada correo electrónico con analogías del mundo real y construya una página de destino de alta conversión para recoger inscripciones. El resultado es un "motor de educación" automatizado que construye su marca personal y proporciona valor real a sus suscriptores cada día. Google Antigravity Prompt: Create a [N-part] email course on [topic, e.g., "Mastering Docker in 7 Days"]: Curriculum Design: Structure [N] emails with progressive difficulty: Each email: 500 words max, 1 key concept, 1 hands-on exercise Subject line A/B variants (2 options per email optimized for open rates) Preview text (90 characters) for inbox display Content Per Email: Day [N] — [Topic]: One clear learning objective Concept (200 words): Explain with a real-world analogy Code Exercise: Minimal, self-contained, 15-minute task Pro Tip: One advanced insight most tutorials miss Homework: Optional stretch challenge linking to next day's topic Resources: Research and include per email: 1 recommended free resource (blog/video) via web search 1 common mistake with debugging steps Landing Page: Create landing_page.html with: Course overview, curriculum outline, signup form placeholder Social proof section (testimonial placeholders) FAQ (5 questions) Save all emails as individual Markdown files to [output directory] Tone: friendly mentor, not formal textbook. Include emoji sparingly. Why This Is a Game-Changer: Los cursos de correo electrónico tienen tasas de finalización 5x más altas que los cursos de vídeo - este formato maximiza los resultados de aprendizaje Las líneas de tema A/B y el texto de vista previa son críticos para las tasas abiertas, pero raramente optimizados por los creadores de contenido. La dificultad progresiva con los micro-exercicios diarios evita la sobrecarga (la razón número uno por la que los estudiantes dejan de practicar) Las páginas de destino con una estructura de prueba social convierten a los visitantes en suscriptores a tasas significativamente más altas ⏱ Time Saved: 20 hours manually → 30 minutes with Antigravity 30: Taller de conferencias / Kit de preparación del taller - La preparación de una conferencia o taller toma decenas de horas, desde la investigación de las últimas tendencias a la redacción de notas del orador y la construcción de planes de retroceso en vivo. - A "live demo fallback" is a safety net (like screenshots or recordings) that you show if the internet fails or your code breaks while you're on stage in front of hundreds of people. - Este prompt tiene Antigravity investigación su audiencia, delinear sus diapositivas, escribir sus notas de orador, y preparar un repositorio "starter" vs. "completo" para los participantes del taller. - El resultado es un "paquete de hablantes" a prueba de balas que te asegura estar tranquilo, preparado y profesional desde el momento en que salgas al escenario. Google Antigravity Prompt: Prepare a complete conference talk/workshop package for [topic, e.g., "Building Production AI Agents with LangGraph"]: Research: Search for: Recent talks on this topic at [conference names/URLs] — identify unique angles Latest developments (last 3 months) via [tech news URLs] Audience profile of [conference name] from past attendee surveys or descriptions Slide Outline: Create [N-slide] presentation outline: Opening (3 slides): Hook question + "By the end, you'll be able to…" + agenda Core Content (15-20 slides): 3-4 key sections with: Concept explanation → live demo script → key takeaway per section Speaker notes (exact words to say per slide, 1-2 min each) Live Demo Plan: Step-by-step script with fallback screenshots if demo fails Closing (3 slides): Summary, resources QR code content, Q&A prompts Workshop Repo (if workshop): Create at [repo path] with: starter/ branch (skeleton code for attendees) complete/ branch (finished working solution) SETUP.md with pre-workshop preparation checklist Handout: Create CHEAT_SHEET.md — single-page reference attendees can take home Bio & Abstract: 3 title options + 200-word abstract + 50-word speaker bio Save all materials to [output directory]. Why This Is a Game-Changer: Conference talk prep averages 40+ hours — this generates 80% of the materials in under an hour Las capturas de pantalla de retroceso de demostración en vivo evitan el escenario de pesadilla de una demostración fallida en el escenario El patrón de rama de inicio/completo es el estándar de oro para talleres, pero rara vez se implementa debido a las restricciones de tiempo Las notas de altavoces con el timing exacto aseguran que la conversación se ajusta a la ranura asignada sin correr por encima o por debajo ⏱ Time Saved: 40 hours manually → 1 hour with Antigravity Conclusión: Un nuevo amanecer en la historia de la computación Mira los 30 prompts arriba. Look at those 30 prompts above. Now count how many of them involve writing a single line of code. Now count how many of them involve writing a single line of code. La respuesta es: ¡la mayoría de ellos no lo hacen! The answer is: most of them don't! A compliance officer auditing GDPR policies. Un estudiante preparándose para un examen de sistemas operativos. Un investigador mapea 200 artículos en tres bases de datos académicas. Un creador de contenido técnico que escribe un tutorial de YouTube con reposo de compañero timestampado. Un gerente corporativo que convierte los CSV de ventas brutas en un informe trimestral con gráficos. Ninguna de estas personas es desarrolladora. None of these people are developers. And yet, every single one of them just had their workflow revolutionized by Google Antigravity. Este es el avance del que nadie está hablando y, francamente, es sorprendente que no haya sido destacado antes. Hemos estado tan ocupados en el marco de la Antigravedad como una "herramienta de codificación" que hemos perdido el cambio tectónico oculto a simple vista. We have been so busy framing Antigravity as a "coding tool" that we missed the tectonic shift hiding in plain sight. Cuando usted da a un agente de AI acceso a su tuyo , y el Y después de dejarlo — No solo has construido un mejor editor de código. local file system web browser open internet plan, execute, verify, and iterate autonomously Has construido una orquesta de trabajo universal. You have built a universal work orchestrator. El ordenador se convierte en la API. Cada archivo, cada carpeta, cada hoja de cálculo, cada pestaña del navegador, cada comando terminal, todo se vuelve orquestable. Piensa en lo que eso significa. Think about what that means. Por primera vez en la historia de la computación personal, la barrera entre "lo que quiero hacer" y "lo que se hace" se ha derrumbado a una única prompt de lenguaje natural. No sólo por el código. por . everything Un abogado puede apuntar a Antigravity a una carpeta de contratos, a un sitio web regulatorio y a un modelo de cumplimiento - y marcharse. Un profesor puede señalarlo en un plan de estudios, un libro de texto y un banco de preguntas, y recibir un paquete completo de exámenes. Un gerente de marketing puede apuntar a los sitios web de los competidores, los datos de ventas internos y una guía de marca, y obtener un resumen de la plataforma con datos de mercado de origen. No se trata de una mejora incremental. Esta es una nueva categoría de software, donde el sistema operativo se convierte en la tela y los agentes de la IA se convierten en el cepillo. This is a new category of software — one where the operating system itself becomes the canvas, and AI agents become the brush. We went from command-line interfaces to graphical interfaces to voice interfaces. Ahora estamos entrando en la era de la interfaz de intenciones: usted declara su intención, y los agentes autónomos orquestan todo su entorno digital para cumplirlo. Now we are entering the intent interface era: you state your intent, and autonomous agents orchestrate your entire digital environment to fulfill it. The sunrise you see on the horizon isn't just for developers. It's for every knowledge worker, every student, every researcher, every creator, every professional who sits in front of a computer screen and thinks, "I wish this could just... get done." “Me gustaría que esto sólo pudiera... hacerse”. Now it can. ¡Y el mundo aún no se ha dado cuenta! And the world hasn't realized it as yet! Referencias Sitio oficial de Google Antigravity — antigravity.google Documentación de Antigravidad de Google — antigravityide.help Blog de Google — Introducción a Antigravidad — blog.google/tecnology/developers Antigravidad Wikipedia Página — en.wikipedia.org/wiki/Google_Antigravidad Especificaciones del protocolo MCP — modelcontextprotocol.io Antropic Claude Documentación — docs.anthropic.com GLM-5 Repositorio de GitHub (Zhipu AI) — github.com/THUDM/GLM Sitio oficial de DeepSeek — deepseek.com Documentación oficial de Kimi K2 — Kimi-k2.ai Ollama (Local LLM Desarrollo) — ollama.com Servicio de Inferencia de vLLM — docs.vllm.ai SGLang Inference Framework — github.com/sgl-project/sglang Kit de desarrollo de agentes de Google (ADK) — google.github.io/adk-docs Codecademy — Google Antigravity Guide — codecademy.com/article/what-is-google-antigravity Marco de Evaluación de RAGAS — docs.ragas.io Documentación LangGraph — langchain-ai.github.io/langgraph Peso y prejuicios — wandb.ai HuggingFace Hub — huggingface.co Integración Composio MCP — composio.dev Analytics Vidhya – Guía de servidores MCP – analyticsvidhya.com/blog/mcp-servers Docusaurus Documentation Framework — Docusaurus.io Generador de Documentación de MkDocs — mkdocs.org VitePress Generador de sitios estáticos — vitepress.dev ConvertKit Email Marketing — convertkit.com Algolia DocSearch — docsearch.algolia.com YouTube Creator Academy — creatoracademy.youtube.com Sessionize (Plataforma de Conferencia CFP) — sessionize.com Todas las imágenes de arriba fueron generadas por Google Nano Banana, versión rápida. All images above were generated by Google Nano Banana, fast version. Claude Opus 4.6 se utilizó para investigar y verificar el artículo de forma exhaustiva. Claude Opus 4.6 was used to research and verify the article thoroughly. Google Antigravity con el Gemini AI Pro Plan se utilizó para el primer proyecto. Google Antigravity with the Gemini AI Pro Plan was used for the first draft.